BRAMS(Brazilian developments on the Regional Atmospheric Modelling System)
4 times per day, from 08:00, 14:00, 20:00, and 00:00 UTC
Greenwich Mean Time:
12:00 UTC = 15:00 MSK
Sea Level Pressure in hPa (solid lines) and equivalent potential temperature at 700 hPa (dashed and coloured)
The equivalent potential temperature map - updated every 6 hours - shows the modelled equivalent
potential temperature at the 850hPa level. The equivalent potential temperature is commonly referred
to as Theta-e (θe). θe is the temperature of a parcel of air after it was lifted until
it became saturated with water vapour (adibatically). When this parcel becomes saturated and
condensation begins, the process of condensation releases latent heat into the surrounding air.
This latent heat further warms the air making the air even more buoyant. We refer to this as a moist
adiabatic or saturated adiabatic process. Moist adiabatic expansion increases the instability of the parcel.
If this process of moist adiabatic expansion continues, all of the water may condense out of the rising
parcel and precipitate out, yielding a dry parcel, and is dropped adiabatically to an atmospheric pressure of
1000 hPa. The potential temperature of that new dry parcel is called the equivalent potential temperature
(θe) of the original moist parcel
In meteorology θe is used to indicate areas with unstable and thus positively buoyant air. The θe of
an air parcel increases with increasing temperature and increasing dewpoint as for the latter more latent
heat that can be released. Therefore, in a region with adequate instability, areas of relatively high θe
(called θe ridges) are often the burst points for thermodynamically induced thunderstorms and MCS's.
θe ridges can often be found in those areas experiencing the greatest warm air advection and moisture advection.
(source: the weather prediction
Keep in mind that if a strong cap is in place, convective storms will not occur even if θe is high.
As different origins of airmasses largely determine their own θe,
one can use this parameter as a marker. Fronts are easily seen as steep gradients in
θe. The boundary layer θe shows where fronts are located near the surface,
while 700 hPa θe shows where they are near the 3000 m level. In winter it occurs
often that warm fronts do not penetrate into the heavy, cold airmass near the surface.
The BRAMS Brazilian developments on the Regional Atmospheric Modelling System is a project originaly developed by ATMET, IME/USP, IAG/USP and CPTEC/INPE, funded by FINEP (Brazilian Funding Agency), aimed to produce a new version of RAMS tailored to the tropics. The main objective is to provide a single model to Brazilian Regional Weather Centers. The BRAMS/RAMS model is a multipurpose, numerical prediction model designed to simulate atmospheric circulations spanning in scale from hemispheric scales down to large eddy simulations (LES) of the planetary boundary layer. After the version 4.2 the code is developed only by CPTEC/INPE team developers. The BRAMS uses the Cathedral model, but code developed between releases is restricted to an exclusive group of software developers. The software is under CC-GNU GPL license and some parts of code may receives other restricted licenses. The BRAMS incorporate a tracer transport model and chemical model (CCATT) and becomes a unified version, BRAMS 5.x.
Numerical weather prediction uses current weather conditions as input into mathematical models of the atmosphere to predict the weather. Although the first efforts to accomplish this were done in the 1920s, it wasn't until the advent of the computer and computer simulation that it was feasible to do in real-time. Manipulating the huge datasets and performing the complex calculations necessary to do this on a resolution fine enough to make the results useful requires the use of some of the most powerful supercomputers in the world. A number of forecast models, both global and regional in scale, are run to help create forecasts for nations worldwide. Use of model ensemble forecasts helps to define the forecast uncertainty and extend weather forecasting farther into the future than would otherwise be possible.
Wikipedia, Numerical weather prediction, http://en.wikipedia.org/wiki/Numerical_weather_prediction
(as of Feb. 9, 2010, 20:50 UTC).